YOLOv8 Data Annotation and Fine-tuning for Object Detection
Fine-tuned the YOLOv8 model for object detection and classification tasks on a custom image dataset. Focused on accurately annotating and labeling bounding boxes for four distinct classes to achieve high model precision. Addressed post-labeling performance using model conversion and deployment strategies for optimized inference. • Ensured consistent and precise bounding box annotation across the dataset. • Maintained class definition quality and data balance for robust training. • Utilized model performance metrics to iterate on labeling completeness. • Coordinated labeling and annotation workflow with downstream deployment processes.